Course overview
Geospatial AI for Smart Cities and Urban Development is a practical, technology-focused course designed to equip professionals with the knowledge and skills required to apply Geographic Information Systems (GIS), Artificial Intelligence (AI), Machine Learning (ML), Remote Sensing, Spatial Analytics, and Location Intelligence to modern urban planning and smart-city development.
The course explores how geospatial data and AI can be used to understand cities, monitor urban growth, optimize infrastructure and services, analyze mobility and transportation, assess environmental conditions, identify development patterns, and support evidence-based urban decision-making.
Participants work with modern geospatial technologies and real-world urban datasets to develop practical solutions for applications such as land-use planning, urban expansion monitoring, transportation planning, infrastructure management, environmental monitoring, disaster risk management, public-service optimization, and smart-city dashboards.
Course objectives
- Understand the principles of Geospatial AI (GeoAI) and its applications in smart cities.
- Identify and prepare geospatial datasets for AI and machine-learning applications
- Work with vector, raster, satellite imagery, GPS, OpenStreetMap and other spatial datasets.
- Perform spatial analysis for urban development and infrastructure planning.
- Analyze transportation and mobility patterns using geospatial data.
- Use GeoAI for infrastructure and public-service planning.
- Understand how real-time sensor and IoT data can support smart-city applications.
- Apply geospatial analytics to environmental and climate-related urban challenges.
- Apply AI to urban land-use and land-cover analysis.
Who should attend
Urban planners,City and county planners,GIS professionals,Geospatial analysts,Remote sensing specialists,Surveyors
Land-use planners,Environmental professionals,Transport and mobility planners,Infrastructure planners,Smart-city professionals,Municipal and county government officers,Government agencies,Development organizations
Engineers,Architects,Real-estate and property professionals,Disaster-risk professionals,Researchers and academics,Data analysts
ICT professionals working on smart-city projects
AI and data science professionals interested in geospatial applications
Professionals involved in urban development and spatial planning
Course content
- Module 1: Introduction to Geospatial AI and Smart Cities Topics What is Geospatial AI? Evolution from GIS to GeoAI GIS, AI, ML and spatial data science Components of a smart city Role of location intelligence in urban development Digital transformation of
- Module 2: Geospatial Data for Urban Planning Topics Spatial data concepts Vector and raster data Point, line and polygon datasets Coordinate reference systems Spatial databases Urban cadastral data Road and transportation networks Building footprints Popu
- Module 2: Geospatial Data for Urban Planning Topics Geospatial data types and structures Vector, raster and 3D geospatial data Urban spatial datasets and data sources Satellite, GPS, drone and IoT data OpenStreetMap and open geospatial data Geospatial d
- Module 4: Remote Sensing for Urban Development Topics Introduction to Earth observation Satellite imagery for urban analysis Sentinel and Landsat datasets High-resolution imagery Spectral bands Vegetation and built-up indices NDVI NDBI NDWI Land-use/land-
- Module 5: Machine Learning for Geospatial Applications Topics Introduction to machine learning Supervised and unsupervised learning Classification Regression Clustering Feature engineering Training and testing datasets Model validation Accuracy assessment
- Module 6: GeoAI for Urban Land-Use Planning Topics AI-assisted land-use classification Urban growth modelling Land-use suitability analysis Development pressure mapping Informal settlement mapping Building-density analysis Population-density modelling Urb
- Module 7: AI and Satellite Imagery Topics Image classification Object detection Image segmentation Deep learning concepts Convolutional Neural Networks Building extraction Road extraction Settlement detection Urban feature detection Change detection using
- Module 8: Smart Transportation and Mobility Analytics Topics GIS for transportation planning Road-network analysis Traffic data GPS trajectory data Public transportation analysis Travel-time analysis Accessibility modelling Traffic hotspot identification
- Module 9: GeoAI for Infrastructure and Public Services Topics Infrastructure mapping Water and sanitation networks Electricity infrastructure Waste-management planning Streetlight planning Health-service accessibility Emergency-service optimization School
- Module 10: GeoAI for Environmental and Climate-Smart Cities Topics Urban environmental monitoring Urban heat islands Air-quality mapping Flood-risk mapping Green-space analysis Urban vegetation monitoring Climate vulnerability Carbon and emissions mapping
- Module 11: 3D GIS, Digital Twins and Urban Modelling Topics Introduction to 3D GIS 3D buildings Terrain modelling City models Digital twins BIM-GIS integration 3D urban visualization Urban simulation Infrastructure visualization Digital-city platforms App
- Module 12: IoT, Real-Time Data and Smart-City Platforms Topics Internet of Things and smart cities IoT sensors Real-time geospatial data Traffic sensors Environmental sensors Smart meters Sensor networks Streaming spatial data GIS and IoT integration Real
- Module 13: Spatial Data Science and GeoAI Workflows Topics Python for geospatial analytics GeoPandas Rasterio Shapely NumPy and Pandas Scikit-learn Spatial databases PostGIS GeoServer APIs Jupyter notebooks Cloud geospatial platforms Google Earth Engine c
- Module 14: Geospatial Dashboards and Location Intelligence Topics Principles of geospatial visualization Interactive maps Urban indicators Spatial KPIs GIS dashboards Power BI and spatial data Web GIS Web maps GeoServer Leaflet Map-based reporting Decisio
- Module 15: GeoAI Governance, Privacy and Ethics Topics Geospatial data governance Location privacy Responsible AI Bias in spatial datasets AI transparency Data quality Data security Responsible use of location data Surveillance considerations Open-data go
Frequently asked questions
1. Will the module cover OpenStreetMap?
Yes. Participants learn how OpenStreetMap can provide useful urban datasets such as roads, buildings, amenities, transport networks and other points of interest.
2. What types of geospatial data are used in urban planning?
Urban planning uses vector, raster, satellite imagery, GPS, drone, population, cadastral, transportation, infrastructure, environmental and IoT datasets.
3. Where can urban geospatial data be obtained?
Data can be obtained from government agencies, municipal authorities, OpenStreetMap, satellite platforms, open-data portals, surveys, GPS devices, drones and IoT systems.
Schedule & fees
| Dates | Location | Duration | Fee (USD) |
|---|
| 19/10/2026 - 30/10/2026 |
Dar-es-saalam |
10 Days |
$1,000 |
| 02/11/2026 - 06/11/2026 |
Nairobi,Kenya |
10 Days |
$2,000 |